A variety of measures of cardiac function are used in clinical practice. The formulae used in their calculations are (1):Stroke Volume(SV)=End Diastolic Volume(EDV)−End Systolic Volume(ESV)Ejection Fraction(EF)=(SV/EDV)×100%Cardiac Output(Q)=SV×Heart Rate(HR)
Assessment of cardiac chamber size, including the left ventricle (LV), is commonly undertaken using cardiac ultrasound (echocardiography), radionuclide angiography (RNA), and cardiac magnetic resonance (CMR) imaging. Each technique measures the change in chamber size with each heartbeat; reflecting the amount of blood ejected with each heartbeat. These measures are then used to estimate cardiac mechanical function. The SV is the fraction of blood ejected with each heartbeat and EF is that fraction divided by the amount of blood at rest or in diastole; as measured using the end diastolic volume (EDV). Cardiac output (Q) reflects the volume of blood over time and is the product of SV multiplied by heart rate (HR).
Presently used techniques to estimate LVEF are costly, have limited access in many regions, and require interpretation by a clinician. Moreover, techniques that use radionuclide pharmaceutics pose a risk to patients and healthcare providers in terms of radiation exposure. Further, there is generally poor correlation between these techniques in terms of the LVEF value obtained. Of the various techniques use, both CMR and RNA are considered to represent “gold standard” methods for assessing LVEF in terms of the value obtained and prognosis. Yet, studies comparing RNA with CMR have found only modest correlation (r=0.87) and significant rates of misclassification between these “gold standard” techniques. For example, a 10% or larger individual difference in LVEF was found in 23% of patients. (2). A technology that can reliably estimate LVEF from a common cardiovascular test that is noninvasive and can be readily implemented (i.e., an ECG) has tremendous clinician ideal.
Algorithms commonly employed in signal processing of cardiac signals are typically rudimentary. They can be improved upon using contemporary techniques that evaluate the detailed characteristics of high-resolution 3D ECG signals in terms of geometric relationships, conduction properties, and other characteristics.
The surface ECG contains detailed information on the electrical properties of the heart. A surface ECG signal represents the summation of the individual action potentials from each and every cardiac cell in syncytium. Hence, global alterations in the surface ECG would be expected to reflect the mechanical function of the heart. Moreover, information related to the conduction properties of myocardial tissue is inherent in the surface ECG. A major challenge is discrimination of the pertinent information from a long quasi-periodic ECG signal while excluding noise contamination.
There is a distinct lack of ECG based algorithms to estimate cardiac chamber size and cardiac mechanical function. Various metrics have been developed to estimate chamber enlargement and cardiac mechanical function. These include estimating chamber size based on the amplitude and duration of ECG features (e.g., left atrial abnormality), estimating cardiac mechanical function based on the presence or absence of Q waves, the presence or absence of prominent conduction delays, and the overall amplitude of ECG signals (e.g., QRS voltage). While each approach appeared promising during the development phase, none has been shown to be useful with independent validation in less select populations. (3-10) Yet, it is desirable for an ECG-based system and method to determine cardiac chamber size and systolic function (i.e., LVEF) due to the utility of this data in screening and for daily clinical decision-making. Moreover, these data have been shown to have important prognostic value.